AIMC Topic: Algorithms

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Predefined-time synchronization of competitive neural networks.

Neural networks : the official journal of the International Neural Network Society
In this paper, the predefined-time synchronization of competitive neural networks (CNNs) is researched based on two different predefined-time stability theorems. In view of the bilayer structure of CNNs, we design two bilayer predefined-time controll...

Comparison of machine learning techniques for the identification of human activities from inertial sensors available in a mobile device after the application of data imputation techniques.

Computers in biology and medicine
Human activity recognition (HAR) is a significant research area due to its wide range of applications in intelligent health systems, security, and entertainment games. Over the past few years, many studies have recognized human daily living activitie...

Classical Machine Learning Versus Deep Learning for the Older Adults Free-Living Activity Classification.

Sensors (Basel, Switzerland)
Physical activity has a strong influence on mental and physical health and is essential in healthy ageing and wellbeing for the ever-growing elderly population. Wearable sensors can provide a reliable and economical measure of activities of daily liv...

Efficient Graph Collaborative Filtering via Contrastive Learning.

Sensors (Basel, Switzerland)
Collaborative filtering (CF) aims to make recommendations for users by detecting user's preference from the historical user-item interactions. Existing graph neural networks (GNN) based methods achieve satisfactory performance by exploiting the high-...

EagleEye: A Worldwide Disease-Related Topic Extraction System Using a Deep Learning Based Ranking Algorithm and Internet-Sourced Data.

Sensors (Basel, Switzerland)
Due to the prevalence of globalization and the surge in people's traffic, diseases are spreading more rapidly than ever and the risks of sporadic contamination are becoming higher than before. Disease warnings continue to rely on censored data, but t...

Machine Learning for Sensorless Temperature Estimation of a BLDC Motor.

Sensors (Basel, Switzerland)
In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed...

Automatic fetal biometry prediction using a novel deep convolutional network architecture.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Fetal biometric measurements face a number of challenges, including the presence of speckle, limited soft-tissue contrast and difficulties in the presence of low amniotic fluid. This work proposes a convolutional neural network for automatic...

The Performance of Post-Fall Detection Using the Cross-Dataset: Feature Vectors, Classifiers and Processing Conditions.

Sensors (Basel, Switzerland)
In this study, algorithms to detect post-falls were evaluated using the cross-dataset according to feature vectors (time-series and discrete data), classifiers (ANN and SVM), and four different processing conditions (normalization, equalization, incr...

Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra.

Sensors (Basel, Switzerland)
Raman spectroscopy is a powerful diagnostic tool in biomedical science, whereby different disease groups can be classified based on subtle differences in the cell or tissue spectra. A key component in the classification of Raman spectra is the applic...

Multitask learning over shared subspaces.

PLoS computational biology
This paper uses constructs from machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach and we hypothesised that learning would b...